Paris Alan, Atia George K, Vosoughi Azadeh, Berman Stephen A
IEEE Trans Biomed Eng. 2017 Aug;64(8):1688-1700. doi: 10.1109/TBME.2016.2606595. Epub 2016 Sep 7.
A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately 1/f behavior in the midfrequencies without infinities.
We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically.
In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators.
The GVZM noise model can be a useful and reliable technique for EEG signal processing.
Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.
神经信号处理的一个特点是从亚细胞离子通道到全脑过程都存在高水平噪声。在本文中，我们基于一族我们称为广义范德齐尔 - 麦克沃特（GVZM）功率谱密度（PSD）的函数，提出了一种新的脑电图（EEG）背景周期图模型。据我们所知，GVZM PSD函数是唯一一种参数相对较少、能在0至30Hz以上高精度匹配记录的EEG PSD且在中频具有近似1/f行为且无无穷大值的EEG噪声模型。
我们使用三种方法验证该模型。首先，我们展示了GVZM PSD如何在最大熵平衡的离子通道群体中出现。其次，我们提出了一类混合自回归模型，其模拟脑背景噪声且其周期图渐近于GVZM PSD。第三，我们提出了两种用于稳态视觉诱发电位（SSVEP）频率的实时估计算法，并对其性能进行统计分析。
在成对比较中，基于GVZM的算法在统计上显示出比两种知名且广泛使用的SSVEP估计器有显著的精度提升。
GVZM噪声模型对于EEG信号处理可能是一种有用且可靠的技术。
理解EEG噪声对于基于EEG的神经学以及诸如实时脑机接口等应用至关重要，这些应用必须从非常短的数据段中做出准确的控制决策。GVZM方法代表了一种理解和管理这种神经噪声的成功新范式。
